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Machine Learning Deep Learning Model Deployment
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[TutsNode.com] - Machine Learning Deep Learning Model Deployment (Size: 1.64 GB) (Files: 180)
[TutsNode.com] - Machine Learning Deep Learning Model Deployment
07 Deploying models on browser using JavaScript and TensorFlow.js
040 JavaScript crash course (optional).mp4
040 javascript-crash-course.zip
040 JavaScript crash course (optional).en.srt
043 tfjs4.zip
041 Adding TensforFlow.js to a web page.en.srt
043 Deploying Keras model on a web page using TensorFlow.js.en.srt
039 Installing Visual Studio Code and Live Server.en.srt
038 TensorFlow.js introduction.en.srt
042 Basic tensor operations using TensorFlow.js.en.srt
042 tfjs2.zip
041 tfjs1.zip
043 Deploying Keras model on a web page using TensorFlow.js.mp4
039 Installing Visual Studio Code and Live Server.mp4
041 Adding TensforFlow.js to a web page.mp4
042 Basic tensor operations using TensorFlow.js.mp4
038 TensorFlow.js introduction.mp4
01 Introduction
001 Introduction.en.srt
002 What is a Model_.en.srt
004 Types of Machine Learning.en.srt
003 How do we create a Model_.en.srt
004 Types of Machine Learning.mp4
001 Introduction.mp4
003 How do we create a Model_.mp4
002 What is a Model_.mp4
06 Deploying NLP models for Twitter sentiment analysis
030 twitter-sentiment-analysis.zip
028 text-classifier.zip.zip
033 twitter-pytorch-rest.zip
037 pytorch-serverless.zip
031 text-classifier-pytorch.zip
028 Creating and saving text classifier and tf-idf models.en.srt
034 text-classifier-tensorflow.zip
036 Serving TensorFlow models serverless.en.srt
026 Converting text to numeric values using bag-of-words model.en.srt
030 Deploying tf-idf and text classifier models for Twitter sentiment analysis.en.srt
027 tf-idf model for converting text to numeric values.en.srt
031 Creating a text classifier using PyTorch.en.srt
037 Serving PyTorch models serverless.en.srt
035 Creating a REST API for TensforFlow models using Flask.en.srt
029 Creating a Twitter developer account.en.srt
034 Creating a text classifier using TensorFlow.en.srt
032 pytorch-nlp-rest.zip
028 Creating and saving text classifier and tf-idf models.mp4
036 Serving TensorFlow models serverless.mp4
030 Deploying tf-idf and text classifier models for Twitter sentiment analysis.mp4
033 Twitter sentiment analysis with PyTorch REST API.mp4
026 Converting text to numeric values using bag-of-words model.mp4
027 tf-idf model for converting text to numeric values.mp4
032 Creating a REST API for the PyTorch NLP model.mp4
031 Creating a text classifier using PyTorch.mp4
037 Serving PyTorch models serverless.mp4
035 Creating a REST API for TensforFlow models using Flask.mp4
029 Creating a Twitter developer account.mp4
034 Creating a text classifier using TensorFlow.mp4
035 tf-nlp-flask-rest.zip
036 tf-serverless.zip
05 Deploying Deep Learning Models
022 tf-serving-save-export.zip
020 Building and deploying PyTorch models.en.srt
020 save-export-reload-pytorch-models.zip
025 pytorch-create-save-onnx.zip
019 Understanding Deep Learning Neural Network.en.srt
021 pytorch-flask.zip
023 Understanding Docker containers.en.srt
024 tf-model-serving.zip
020 Building and deploying PyTorch models.mp4
024 Creating a REST API using TensorFlow Model Server.mp4
022 Saving & loading TensorFlow Keras models.mp4
021 Creating a REST API for the PyTorch Model.mp4
019 Understanding Deep Learning Neural Network.mp4
025 Converting a PyTorch model to TensorFlow format using ONNX.mp4
023 Understanding Docker containers.mp4
02 Building, evaluating and saving a Model
005 Creating a Spyder development environment.en.srt
006 Python NumPy Pandas Matplotlib crash course.en.srt
006 python-np-pd-plt.zip
007 git-hub-link.txt
007 ml-classification.zip
008 Saving the Model and the Scaler.en.srt
006 Python NumPy Pandas Matplotlib crash course.mp4
007 Building and evaluating a Classification Model.mp4
008 Saving the Model and the Scaler.mp4
005 Creating a Spyder development environment.mp4
08 Model as a mathematical formula
044 Deriving formula from a Linear Regression Model.en.srt
044 linear-regression.zip
044 Deriving formula from a Linear Regression Model.mp4
04 Creating a REST API for the Machine Learning Model
016 Serverless Machine Learning API using Cloud Functions.en.srt
011 flask-hello-world.zip
012 classifier-rest-service.zip
013 Signing up for a Google Cloud free trial.en.srt
014 classifier-rest-service-on-GCP.zip
015 Deleting the VM instance.en.srt
016 google-cloud-functions-serverless-ml.zip
011 Flask REST API Hello World.en.srt
018 Postman REST client.en.srt
017 colab-rest-api.zip
012 Creating a REST API for the Model.en.srt
017 Creating a REST API on Google Colab.en.srt
016 Serverless Machine Learning API using Cloud Functions.mp4
014 Hosting the Machine Learning REST API on the Cloud.mp4
012 Creating a REST API for the Model.mp4
011 Flask REST API Hello World.mp4
017 Creating a REST API on Google Colab.mp4
018 Postman REST client.mp4
013 Signing up for a Google Cloud free trial.mp4
015 Deleting the VM instance.mp4
03 Deploying the Model in other environments
009 use-model.zip
010 use-model-colab.zip
010 Using the Model in Google Colab environment.en.srt
009 Predicting locally with deserialized Pickle objects.en.srt
010 Using the Model in Google Colab environment.mp4
009 Predicting locally with deserialized Pickle objects.mp4
09 MLOps and MLflow
047 Tracking Model training experiments with MLfLow.en.srt
050 mlflow-pytorch.zip
049 running-mlflow-on-colab.zip
049 Running MLflow on Colab.en.srt
050 Tracking PyTorch experiments with MLflow.en.srt
051 Deploying Models with MLflow.en.srt
046 MLflow Introduction.en.srt
048 Why track ML experiments_.en.srt
047 ml-pipeline-mlfow.zip
051 mlflow-deploy.zip
047 Tracking Model training experiments with MLfLow.mp4
049 Running MLflow on Colab.mp4
050 Tracking PyTorch experiments with MLflow.mp4
051 Deploying Models with MLflow.mp4
045 Machine Learning Operations (MLOps).mp4
046 MLflow Introduction.mp4
048 Why track ML experiments_.mp4
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